This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The discovery of genes predisposing to complex diseases, such as bipolar disorder and schizophrenia, has proven to be very difficult. Over the years, genome linkage scans have identified a number of regions that may harbor candidate genes, yet none of these linkage findings has led to cloning of causative genes for either disorder. This may be due to the modest nature of the linkage signals and the broad genetic regions they encompass, a likely result of the well-known clinical and genetic heterogeneity associated with bipolar disorder, schizophrenia, and psychiatric illness in general. Researchers have now turned to alternative strategies, such as the use of endophenotypes, candidate gene studies, and large-scale genomic association studies, in the hope that these methods may aid in the genetic dissection of complex disorders. We have undertaken several such projects to discover the underlying determinants of bipolar disorder and schizophrenia. In particular, as part of the Consortium on the Genetics of Schizophrenia (COGS) we have collected 12 endophenotypes for schizophrenia and are pursuing linkage studies, as well as a large scale candidate gene study through the use of a custom gene chip that has just complete genotyping. We will also pursue gene-gene interaction studies guided by the results of our genetic network analyses. Additionally, as part of the NIMH Genetics Initiative for Bipolar Disorder, which is participating in the genotyping initiative sponsored by the Genetic Association Information Network (GAIN), we will pursue whole genome association studies of bipolar disorder and related phenotypes. We anticipate that we will receive the genetic data for this project in October. As these are rather large-scale efforts, the computing requirements will be substantial, exceeding our current capabilities as far as efficiency is concerned.